Zero-shot image tagging: Instead of assigning one unseen label to an image during recognition task, zero-shot tagging allows to tag multiple unseen tags to an image and/or ranking the array of unseen tags. Very few papers addressed the zero-shot version of this problem [1]}, [2]}, [3]}. Li et al. [1]} applied the idea of [5]} in tagging. They argued that semantic embeddings (like word2vec) of all possible tags may not be available, and therefore, proposed a hierarchical semantic embedding method for those unavailable tags based on its ancestor classes in WordNet hierarchy. [2]} considered the power set of fixed unseen tags as the label set to perform transductive multi-label learning. Recently, [3]} proposed a fast zero-shot tagging approach that can rank both seen and arbitrary unseen tags during the testing stage. All previous attempts are not end-to-end because they preform training on the top of pre-trained CNN features. In this paper, we propose an end-to-end method for seen detection with zero-shot object tagging.
